Infrastructure-agnostic performance of computation sequences

ABSTRACT

System and methods are described for performing sequences of computations in an infrastructure-agnostic manner. In one implementation, a method comprises: receiving a dispatch request for executing a user-defined pipeline; computing a performance metric based on the dispatch request; and determining, based at least partially on the performance metric, whether to execute the user-defined pipeline locally by the pipeline engine or transmit the dispatch request back to the network adapter.

COPYRIGHT NOTICE

A portion of the disclosure of this patent document contains materialwhich is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the United States Patent andTrademark Office patent file or records, but otherwise reserves allcopyright rights whatsoever.

TECHNICAL FIELD

One or more implementations relate to distributed storage and dataprocessing systems, and, more specifically, to performing sequences ofcomputations in a distributed computation environment.

BACKGROUND

Distributed computing systems utilize multiple components located ondifferent machines to coordinate actions in a manner that appears as asingle coherent system to end-users. In current systems, users musteither choose an idiom of computation (e.g., a map-reduce model) or acommunication idiom (e.g., streaming data). In either of these settings,the user must understand the architecture in which they will beoperating. For example, with a map-reduce model, the user mustunderstand how the map-reduce model operates and how to structure theirsolutions to fit this model. As a result, the user must spend asignificant amount of time becoming familiar with the architecture andits idioms. Moreover, current systems suffer from scale limitations thatrequire that all components scale up or down together, rather thanindividual components scaling independently.

BRIEF DESCRIPTION OF THE DRAWINGS

The included drawings are for illustrative purposes and serve to provideexamples of possible structures and operations for the disclosedinventive systems, apparatus, methods, and computer-readable storagemedia. These drawings in no way limit any changes in form and detailthat may be made by one skilled in the art without departing from thespirit and scope of the disclosed implementations.

FIG. 1A shows a block diagram of an example environment in which anon-demand database service can be used according to someimplementations.

FIG. 1B shows a block diagram of example implementations of elements ofFIG. 1A and example interconnections between these elements according tosome implementations.

FIG. 2A shows a system diagram of example architectural components of anon-demand database service environment according to someimplementations.

FIG. 2B shows a system diagram further illustrating examplearchitectural components of an on-demand database service environmentaccording to some implementations.

FIG. 3 illustrates a diagrammatic representation of a machine in theexemplary form of a computer system within which one or moreimplementations may be carried out.

FIG. 4 illustrates an exemplary pipeline architecture for processinguser-defined pipelines according to some implementations.

FIG. 5 is a flow diagram illustrating an exemplary method for processingdispatch requests from a network adapter according to someimplementations.

FIG. 6 is a flow diagram illustrating an exemplary method for performingsequences of computations in an infrastructure-agnostic manner accordingto some implementations.

DETAILED DESCRIPTION

The implementations described herein relate to systems and methods forperforming sequences of computations in a distributed computationenvironment in an architecture-agnostic manner. Specifically, thesystems and methods utilize a pipeline engine that abstracts both thecomputational definition layer and the network layer. At the user end, auser defines a series of computations to be evaluated independent of acomputational idiom, such as the map-reduce model, and withoutspecifying an underlying computational infrastructure on which thecomputations will be executed. The pipeline engine, which receives arequest to execute the pipeline, can make a determination as to whetherthe pipeline should be executed locally, or whether the pipeline shouldbe executed by a different device on the network. The determination maybe made based on comparisons drawn between local execution speed anddistributed execution speed derived from, for example, measures ofnetwork latency. Moreover, the pipeline engine may utilize externalresources, such as databases or deep learning frameworks, to improve oroptimize local performance. This allows the user to focus on thecomputational problem itself without requiring the user to specify howthe computation is run or distributed across the network.

In an example implementation, the user first specifies one or moreseries of computations (each referred to as a “stage”). If the userprovides more than one stage, they further provide a routine forserializing or deserializing the data that flows between the stages. Thecollection of stages is referred to herein as a “pipeline.” In suchimplementations, the user may provide the following information, whichmay be the minimum information provided: a stage-wise computation and amethod for serializing and deserializing the data (where serializationand deserialization routines, collectively referred to as “serializationroutines,” are used between stages, or at the beginning and end of thepipeline, to format data appropriately). The user may structurecomputations however they wish, including how to ingest data, filterdata, map data, apply functions to the data, and transmit the data forstorage or further use. The user need not understand how the data istransmitted between stages, nor specify how the data is transmitted. Theuser may further build a network adapter (or use a pre-built networkadapter) that handles the underlying network infrastructure andcommunication requirements. This allows a given pipeline to beexecutable on any distributed system infrastructure, such as Hadoop orSpark, a Kafka topology, a Storm topology, etc.

The implementations described herein address the various limitations ofcurrent systems by providing a distributed system framework for whichthe idiom of computation is specified/defined by the user, rather thanspecified/defined by the infrastructure, and the infrastructure can beadapted for any style of network communication. For example, ApacheHadoop and Apache Spark require the user to decompose their problem intoa map-reduce style idiom where data is transformed in some manner andthen reduced to a singular value or values. In between each step ofcomputation, the user may need to specify how to group their data, andare required to use specific serialization formats. Similarly, otherapproaches, such as Apache Kafka and Apache Storm, restrict howcomputations are coordinated across nodes, often resulting in highnetwork overhead. The present implementations provide the user freedomas to both the specific serialization formats they wish to use, and donot restrict how computations are coordinated.

Other platforms, such as Apache Beam, utilize a domain-specificlanguage, where computations and functions specified by the user aretranslated to be compatible with other platforms. Certainimplementations of the present disclosure utilize a pipeline engine thatexecutes computations directly without performing any translations.Moreover, the pipeline engine itself can be adapted to run on variousplatforms.

In some implementations, a number of delegate objects are provided tothe user-defined pipeline that allow for the pipeline to interact withexternal resources during execution. Delegate objects can operate as atype of plugin infrastructure that allows system maintainers to makeoptimizations when using external resources (such as databases, deeplearning frameworks, or other services), such as batching and queuingrequests to improve throughput, as well as send requests to specializedhardware without the user being aware or requiring such at the time theyrequest to have the pipeline executed.

Advantages of the implementations of the disclosure over current systemsinclude, but are not limited to: (1) users need not spend time orresources restricting themselves to particular computational idioms; (2)users are not required to follow any particular communication patterns(e.g., streaming or batched communication); (3) the system may scaleindividual pieces corresponding to specific stages of the user-definedpipeline independently from the rest of the system, rather than scaleall components of the system in an “all-or-nothing” fashion; (4);computations may be executed directly by a pipeline engine withouttranslating the computations for execution on different computationalplatforms; (5) the user need not specify or be concerned with theunderlying infrastructure used, and the system may switch theinfrastructure without requiring additional user input; and (6) thepipeline engine can determine whether to execute pipelines locally orhave the pipeline executed by a different device and/or on a differentinfrastructure based on an evaluation of various performance networks.

Examples of systems, apparatuses, computer-readable storage media, andmethods according to the disclosed implementations are described in thissection. These examples are being provided solely to add context and aidin the understanding of the disclosed implementations. It will thus beapparent to one skilled in the art that the disclosed implementationsmay be practiced without some or all of the specific details provided.In other instances, certain process or method operations, also referredto herein as “blocks,” have not been described in detail in order toavoid unnecessarily obscuring the disclosed implementations. Otherimplementations and applications also are possible, and as such, thefollowing examples should not be taken as definitive or limiting eitherin scope or setting.

In the following detailed description, references are made to theaccompanying drawings, which form a part of the description and in whichare shown, by way of illustration, specific implementations. Althoughthese disclosed implementations are described in sufficient detail toenable one skilled in the art to practice the implementations, it is tobe understood that these examples are not limiting, such that otherimplementations may be used and changes may be made to the disclosedimplementations without departing from their spirit and scope. Forexample, the blocks of the methods shown and described herein are notnecessarily performed in the order indicated in some otherimplementations. Additionally, in some other implementations, thedisclosed methods may include more or fewer blocks than are described.As another example, some blocks described herein as separate blocks maybe combined in some other implementations. Conversely, what may bedescribed herein as a single block may be implemented in multiple blocksin some other implementations. Additionally, the conjunction “or” isintended herein in the inclusive sense where appropriate unlessotherwise indicated; that is, the phrase “A, B, or C” is intended toinclude the possibilities of “A,” “B,” “C,” “A and B,” “B and C,” “A andC,” and “A, B, and C.”

The words “example” or “exemplary” are used herein to mean serving as anexample, instance, or illustration. Any aspect or design describedherein as an “example” or “exemplary” is not necessarily to be construedas preferred or advantageous over other aspects or designs. Rather, useof the words “example” or “exemplary” is intended to present concepts ina concrete fashion.

In addition, the articles “a” and “an” as used herein and in theappended claims should generally be construed to mean “one or more”unless specified otherwise or clear from context to be directed to asingular form. Reference throughout this specification to “animplementation,” “one implementation,” “some implementations,” or“certain implementations” indicates that a particular feature,structure, or characteristic described in connection with theimplementation is included in at least one implementation. Thus, theappearances of the phrase “an implementation,” “one implementation,”“some implementations,” or “certain implementations” in variouslocations throughout this specification are not necessarily allreferring to the same implementation.

Some portions of the detailed description may be presented in terms ofalgorithms and symbolic representations of operations on data bitswithin a computer memory. These algorithmic descriptions andrepresentations are the manner used by those skilled in the dataprocessing arts to most effectively convey the substance of their workto others skilled in the art. An algorithm is herein, and generally,conceived to be a self-consistent sequence of steps leading to a desiredresult. The steps are those requiring physical manipulations of physicalquantities. Usually, though not necessarily, these quantities take theform of electrical or magnetic signals capable of being stored,transferred, combined, compared, or otherwise manipulated. It has provenconvenient at times, principally for reasons of common usage, to referto these signals as bits, values, elements, symbols, characters, terms,numbers, or the like.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise as apparent from the following discussion,it is appreciated that throughout the description, discussions utilizingterms such as “receiving,” “retrieving,” “transmitting,” “computing,”“executing,” “generating,” “processing,” “reprocessing,” “adding,”“subtracting,” “multiplying,” “dividing,” “optimizing,” “calibrating,”“detecting,” “performing,” “analyzing,” “determining,” “enabling,”“identifying,” “modifying,” “transforming,” “applying,” “aggregating,”“extracting,” “registering,” “querying,” “populating,” “hydrating,”“updating,” “mapping,” “causing,” “storing,” “prioritizing,” “queuing,”“managing,” “serializing,” “deserializing,” or the like, refer to theactions and processes of a computer system, or similar electroniccomputing device, that manipulates and transforms data represented asphysical (e.g., electronic) quantities within the computer system'sregisters and memories into other data similarly represented as physicalquantities within the computer system memories or registers or othersuch information storage, transmission, or display devices.

The specific details of the specific aspects of implementationsdisclosed herein may be combined in any suitable manner withoutdeparting from the spirit and scope of the disclosed implementations.However, other implementations may be directed to specificimplementations relating to each individual aspect, or specificcombinations of these individual aspects. Additionally, while thedisclosed examples are often described herein with reference to animplementation in which an on-demand database service environment isimplemented in a system having an application server providing a frontend for an on-demand database service capable of supporting multipletenants, the present implementations are not limited to multi-tenantdatabases or deployment on application servers. Implementations may bepracticed using other database architectures, i.e., ORACLE®, DB2® byIBM, and the like without departing from the scope of theimplementations claimed. Moreover, the implementations are applicable toother systems and environments including, but not limited to,client-server models, mobile technology and devices, wearable devices,and on-demand services.

It should also be understood that some of the disclosed implementationscan be embodied in the form of various types of hardware, software,firmware, or combinations thereof, including in the form of controllogic, and using such hardware or software in a modular or integratedmanner. Other ways or methods are possible using hardware and acombination of hardware and software. Any of the software components orfunctions described in this application can be implemented as softwarecode to be executed by one or more processors using any suitablecomputer language such as, for example, C, C++, Java™ (which is atrademark of Sun Microsystems, Inc.), or Perl using, for example,existing or object-oriented techniques. The software code can be storedas non-transitory instructions on any type of tangible computer-readablestorage medium (referred to herein as a “non-transitorycomputer-readable storage medium”). Examples of suitable media includerandom access memory (RAM), read-only memory (ROM), magnetic media suchas a hard-drive or a floppy disk, or an optical medium such as a compactdisc (CD) or digital versatile disc (DVD), flash memory, and the like,or any combination of such storage or transmission devices.Computer-readable media encoded with the software/program code may bepackaged with a compatible device or provided separately from otherdevices (for example, via Internet download). Any such computer-readablemedium may reside on or within a single computing device or an entirecomputer system, and may be among other computer-readable media within asystem or network. A computer system, or other computing device, mayinclude a monitor, printer, or other suitable display for providing anyof the results mentioned herein to a user.

The disclosure also relates to apparatuses, devices, and systemadapted/configured to perform the operations herein. The apparatuses,devices, and systems may be specially constructed for their requiredpurposes, may be selectively activated or reconfigured by a computerprogram, or some combination thereof.

EXAMPLE SYSTEM OVERVIEW

FIG. 1A shows a block diagram of an example of an environment 10 inwhich an on-demand database service can be used in accordance with someimplementations. The environment 10 includes user systems 12, a network14, a database system 16 (also referred to herein as a “cloud-basedsystem”), a processor system 17, an application platform 18, a networkinterface 20, tenant database 22 for storing tenant data 23, systemdatabase 24 for storing system data 25, program code 26 for implementingvarious functions of the database system 16, and process space 28 forexecuting database system processes and tenant-specific processes, suchas running applications as part of an application hosting service. Insome other implementations, environment 10 may not have all of thesecomponents or systems, or may have other components or systems insteadof, or in addition to, those listed above.

In some implementations, the environment 10 is an environment in whichan on-demand database service exists. An on-demand database service,such as that which can be implemented using the database system 16, is aservice that is made available to users outside an enterprise (orenterprises) that owns, maintains, or provides access to the databasesystem 16. An “enterprise” refers generally to a company or organizationthat owns one or more data centers that host various services and datasources. A “data center” refers generally to a physical location ofvarious servers, machines, and network components utilized by anenterprise.

As described above, such users generally do not need to be concernedwith building or maintaining the database system 16. Instead, resourcesprovided by the database system 16 may be available for such users' usewhen the users need services provided by the database system 16; thatis, on the demand of the users. Some on-demand database services canstore information from one or more tenants into tables of a commondatabase image to form a multi-tenant database system (MTS). The term“multi-tenant database system” can refer to those systems in whichvarious elements of hardware and software of a database system may beshared by one or more customers or tenants. For example, a givenapplication server may simultaneously process requests for a greatnumber of customers, and a given database table may store rows of datasuch as feed items for a potentially much greater number of customers. Adatabase image can include one or more database objects. A relationaldatabase management system (RDBMS) or the equivalent can execute storageand retrieval of information against the database object(s).

Application platform 18 can be a framework that allows the applicationsof the database system 16 to execute, such as the hardware or softwareinfrastructure of the database system 16. In some implementations, theapplication platform 18 enables the creation, management and executionof one or more applications developed by the provider of the on-demanddatabase service, users accessing the on-demand database service viauser systems 12, or third party application developers accessing theon-demand database service via user systems 12.

In some implementations, the database system 16 implements a web-basedcustomer relationship management (CRM) system. For example, in some suchimplementations, the database system 16 includes application serversconfigured to implement and execute CRM software applications as well asprovide related data, code, forms, renderable web pages, and documentsand other information to and from user systems 12 and to store to, andretrieve from, a database system related data, objects, and Web pagecontent. In some MTS implementations, data for multiple tenants may bestored in the same physical database object in tenant database 22. Insome such implementations, tenant data is arranged in the storagemedium(s) of tenant database 22 so that data of one tenant is keptlogically separate from that of other tenants so that one tenant doesnot have access to another tenant's data, unless such data is expresslyshared. The database system 16 also implements applications other than,or in addition to, a CRM application. For example, the database system16 can provide tenant access to multiple hosted (standard and custom)applications, including a CRM application. User (or third partydeveloper) applications, which may or may not include CRM, may besupported by the application platform 18. The application platform 18manages the creation and storage of the applications into one or moredatabase objects and the execution of the applications in one or morevirtual machines in the process space of the database system 16.

According to some implementations, each database system 16 is configuredto provide web pages, forms, applications, data, and media content touser (client) systems 12 to support the access by user systems 12 astenants of the database system 16. As such, the database system 16provides security mechanisms to keep each tenant's data separate unlessthe data is shared. If more than one MTS is used, they may be located inclose proximity to one another (for example, in a server farm located ina single building or campus), or they may be distributed at locationsremote from one another (for example, one or more servers located incity A and one or more servers located in city B). As used herein, eachMTS could include one or more logically or physically connected serversdistributed locally or across one or more geographic locations.Additionally, the term “server” is meant to refer to a computing deviceor system, including processing hardware and process space(s), anassociated storage medium such as a memory device or database, and, insome instances, a database application, such as an object-orienteddatabase management system (OODBMS) or a relational database managementsystem (RDBMS), as is well known in the art. It should also beunderstood that “server system” and “server” are often usedinterchangeably herein. Similarly, the database objects described hereincan be implemented as part of a single database, a distributed database,a collection of distributed databases, a database with redundant onlineor offline backups or other redundancies, etc., and can include adistributed database or storage network and associated processingintelligence.

The network 14 can be or include any network or combination of networksof systems or devices that communicate with one another. For example,the network 14 can be or include any one or any combination of a localarea network (LAN), wide area network (WAN), telephone network, wirelessnetwork, cellular network, point-to-point network, star network, tokenring network, hub network, or other appropriate configuration. Thenetwork 14 can include a Transfer Control Protocol and Internet Protocol(TCP/IP) network, such as the global internetwork of networks oftenreferred to as the “Internet” (with a capital “I”). The Internet will beused in many of the examples herein. However, it should be understoodthat the networks that the disclosed implementations can use are not solimited, although TCP/IP is a frequently implemented protocol.

The user systems 12 can communicate with the database system 16 usingTCP/IP and, at a higher network level, other common Internet protocolsto communicate, such as the Hyper Text Transfer Protocol (HTTP), HyperText Transfer Protocol Secure (HTTPS), File Transfer Protocol (FTP),Apple File Service (AFS), Wireless Application Protocol (WAP), etc. Inan example where HTTP is used, each user system 12 can include an HTTPclient commonly referred to as a “web browser” or simply a “browser” forsending and receiving HTTP signals to and from an HTTP server of thedatabase system 16. Such an HTTP server can be implemented as the solenetwork interface 20 between the database system 16 and the network 14,but other techniques can be used in addition to or instead of thesetechniques. In some implementations, the network interface 20 betweenthe database system 16 and the network 14 includes load sharingfunctionality, such as round-robin HTTP request distributors to balanceloads and distribute incoming HTTP requests evenly over a number ofservers. In MTS implementations, each of the servers can have access tothe MTS data; however, other alternative configurations may be usedinstead.

The user systems 12 can be implemented as any computing device(s) orother data processing apparatus or systems usable by users to access thedatabase system 16. For example, any of user systems 12 can be a desktopcomputer, a work station, a laptop computer, a tablet computer, ahandheld computing device, a mobile cellular phone (for example, a“smartphone”), or any other Wi-Fi-enabled device, WAP-enabled device, orother computing device capable of interfacing directly or indirectly tothe Internet or other network. When discussed in the context of a user,the terms “user system,” “user device,” and “user computing device” areused interchangeably herein with one another and with the term“computer.” As described above, each user system 12 typically executesan HTTP client, for example, a web browsing (or simply “browsing”)program, such as a web browser based on the WebKit platform, Microsoft'sInternet Explorer browser, Netscape's Navigator browser, Opera'sbrowser, Mozilla's Firefox browser, or a WAP-enabled browser in the caseof a cellular phone, personal digital assistant (PDA), or other wirelessdevice, allowing a user (for example, a subscriber of on-demand servicesprovided by the database system 16) of the user system 12 to access,process, and view information, pages, and applications available to itfrom the database system 16 over the network 14.

Each user system 12 also typically includes one or more user inputdevices, such as a keyboard, a mouse, a trackball, a touch pad, a touchscreen, a pen or stylus, or the like, for interacting with a GUIprovided by the browser on a display (for example, a monitor screen,liquid crystal display (LCD), light-emitting diode (LED) display, etc.)of the user system 12 in conjunction with pages, forms, applications,and other information provided by the database system 16 or othersystems or servers. For example, the user interface device can be usedto access data and applications hosted by database system 16, and toperform searches on stored data, or otherwise allow a user to interactwith various GUI pages that may be presented to a user. As discussedabove, implementations are suitable for use with the Internet, althoughother networks can be used instead of or in addition to the Internet,such as an intranet, an extranet, a virtual private network (VPN), anon-TCP/IP based network, any LAN or WAN or the like.

The users of user systems 12 may differ in their respective capacities,and the capacity of a particular user system 12 can be entirelydetermined by permissions (permission levels) for the current user ofsuch user system. For example, where a salesperson is using a particularuser system 12 to interact with the database system 16, that user systemcan have the capacities allotted to the salesperson. However, while anadministrator is using that user system 12 to interact with the databasesystem 16, that user system can have the capacities allotted to thatadministrator. Where a hierarchical role model is used, users at onepermission level can have access to applications, data, and databaseinformation accessible by a lower permission level user, but may nothave access to certain applications, database information, and dataaccessible by a user at a higher permission level. Thus, different usersgenerally will have different capabilities with regard to accessing andmodifying application and database information, depending on the users'respective security or permission levels (also referred to as“authorizations”).

According to some implementations, each user system 12 and some or allof its components are operator-configurable using applications, such asa browser, including computer code executed using a central processingunit (CPU), such as an Intel Pentium® processor or the like. Similarly,the database system 16 (and additional instances of an MTS, where morethan one is present) and all of its components can beoperator-configurable using application(s) including computer code torun using the processor system 17, which may be implemented to include aCPU, which may include an Intel Pentium® processor or the like, ormultiple CPUs.

The database system 16 includes non-transitory computer-readable storagemedia having instructions stored thereon that are executable by or usedto program a server or other computing system (or collection of suchservers or computing systems) to perform some of the implementation ofprocesses described herein. For example, the program code 26 can includeinstructions for operating and configuring the database system 16 tointercommunicate and to process web pages, applications, and other dataand media content as described herein. In some implementations, theprogram code 26 can be downloadable and stored on a hard disk, but theentire program code, or portions thereof, also can be stored in anyother volatile or non-volatile memory medium or device as is well known,such as a ROM or RAM, or provided on any media capable of storingprogram code, such as any type of rotating media including floppy disks,optical discs, DVDs, CDs, microdrives, magneto-optical discs, magneticor optical cards, nanosystems (including molecular memory integratedcircuits), or any other type of computer-readable medium or devicesuitable for storing instructions or data. Additionally, the entireprogram code, or portions thereof, may be transmitted and downloadedfrom a software source over a transmission medium, for example, over theInternet, or from another server, as is well known, or transmitted overany other existing network connection as is well known (for example,extranet, VPN, LAN, etc.) using any communication medium and protocols(for example, TCP/IP, HTTP, HTTPS, Ethernet, etc.) as are well known. Itwill also be appreciated that computer code for the disclosedimplementations can be realized in any programming language that can beexecuted on a server or other computing system such as, for example, C,C++, HTML, any other markup language, Java™, JavaScript, ActiveX, anyother scripting language, such as VBScript, and many other programminglanguages as are well known.

FIG. 1B shows a block diagram of example implementations of elements ofFIG. 1A and example interconnections between these elements according tosome implementations. That is, FIG. 1B also illustrates environment 10,but FIG. 1B, various elements of the database system 16 and variousinterconnections between such elements are shown with more specificityaccording to some more specific implementations. In someimplementations, the database system 16 may not have the same elementsas those described herein or may have other elements instead of, or inaddition to, those described herein.

In FIG. 1B, the user system 12 includes a processor system 12A, a memorysystem 12B, an input system 12C, and an output system 12D. The processorsystem 12A can include any suitable combination of one or moreprocessors. The memory system 12B can include any suitable combinationof one or more memory devices. The input system 12C can include anysuitable combination of input devices, such as one or more touchscreeninterfaces, keyboards, mice, trackballs, scanners, cameras, orinterfaces to networks. The output system 12D can include any suitablecombination of output devices, such as one or more display devices,printers, or interfaces to networks.

In FIG. 1B, the network interface 20 is implemented as a set of HTTPapplication servers 100 ₁-100 _(N). Each application server 100, alsoreferred to herein as an “app server,” is configured to communicate withtenant database 22 and the tenant data 23 therein, as well as systemdatabase 24 and the system data 25 therein, to serve requests receivedfrom the user systems 12. The tenant data 23 can be divided intoindividual tenant storage spaces 112, which can be physically orlogically arranged or divided. Within each tenant storage space 112,user storage 114, and application metadata 116 can similarly beallocated for each user. For example, a copy of a user's most recentlyused (MRU) items can be stored to user storage 114. Similarly, a copy ofMRU items for an entire organization that is a tenant can be stored totenant storage space 112.

The database system 16 also includes a user interface (UI) 30 and anapplication programming interface (API) 32. The process space 28includes system process space 102, individual tenant process spaces 104and a tenant management process space 110. The application platform 18includes an application setup mechanism 38 that supports applicationdevelopers' creation and management of applications. Such applicationsand others can be saved as metadata into tenant database 22 by saveroutines 36 for execution by subscribers as one or more tenant processspaces 104 managed by tenant management process space 110, for example.Invocations to such applications can be coded using PL/SOQL 34, whichprovides a programming language style interface extension to the API 32.A detailed description of some PL/SOQL language implementations isdiscussed in commonly assigned U.S. Pat. No. 7,730,478, titled METHODAND SYSTEM FOR ALLOWING ACCESS TO DEVELOPED APPLICATIONS VIA AMULTI-TENANT ON-DEMAND DATABASE SERVICE, issued on Jun. 1, 2010, andhereby incorporated by reference herein in its entirety and for allpurposes. Invocations to applications can be detected by one or moresystem processes, which manage retrieving application metadata 116 forthe subscriber making the invocation and executing the metadata as anapplication in a virtual machine.

Each application server 100 can be communicably coupled with tenantdatabase 22 and system database 24, for example, having access to tenantdata 23 and system data 25, respectively, via a different networkconnection. For example, one application server 100 ₁ can be coupled viathe network 14 (for example, the Internet), another application server100 ₂ can be coupled via a direct network link, and another applicationserver 100 _(N) can be coupled by yet a different network connection.Transfer Control Protocol and Internet Protocol (TCP/IP) are examples oftypical protocols that can be used for communicating between applicationservers 100 and the database system 16. However, it will be apparent toone skilled in the art that other transport protocols can be used tooptimize the database system 16 depending on the networkinterconnections used.

In some implementations, each application server 100 is configured tohandle requests for any user associated with any organization that is atenant of the database system 16. Because it can be desirable to be ableto add and remove application servers 100 from the server pool at anytime and for various reasons, in some implementations there is no serveraffinity for a user or organization to a specific application server100. In some such implementations, an interface system implementing aload balancing function (for example, an F5 Big-IP load balancer) iscommunicably coupled between the application servers 100 and the usersystems 12 to distribute requests to the application servers 100. In oneimplementation, the load balancer uses a least-connections algorithm toroute user requests to the application servers 100. Other examples ofload balancing algorithms, such as round robin andobserved-response-time, also can be used. For example, in someinstances, three consecutive requests from the same user could hit threedifferent application servers 100, and three requests from differentusers could hit the same application server 100. In this manner, by wayof example, database system 16 can be a multi-tenant system in whichdatabase system 16 handles storage of, and access to, different objects,data, and applications across disparate users and organizations.

In one example storage use case, one tenant can be a company thatemploys a sales force where each salesperson uses database system 16 tomanage aspects of their sales. A user can maintain contact data, leadsdata, customer follow-up data, performance data, goals and progressdata, etc., all applicable to that user's personal sales process (forexample, in tenant database 22). In an example of a MTS arrangement,because all of the data and the applications to access, view, modify,report, transmit, calculate, etc., can be maintained and accessed by auser system 12 having little more than network access, the user canmanage his or her sales efforts and cycles from any of many differentuser systems. For example, when a salesperson is visiting a customer andthe customer has Internet access in their lobby, the salesperson canobtain critical updates regarding that customer while waiting for thecustomer to arrive in the lobby.

While each user's data can be stored separately from other users' dataregardless of the employers of each user, some data can beorganization-wide data shared or accessible by several users or all ofthe users for a given organization that is a tenant. Thus, there can besome data structures managed by database system 16 that are allocated atthe tenant level while other data structures can be managed at the userlevel. Because an MTS can support multiple tenants including possiblecompetitors, the MTS can have security protocols that keep data,applications, and application use separate. Also, because many tenantsmay opt for access to an MTS rather than maintain their own system,redundancy, up-time, and backup are additional functions that can beimplemented in the MTS. In addition to user-specific data andtenant-specific data, the database system 16 also can maintain systemlevel data usable by multiple tenants or other data. Such system leveldata can include industry reports, news, postings, and the like that aresharable among tenants.

In some implementations, the user systems 12 (which also can be clientsystems) communicate with the application servers 100 to request andupdate system-level and tenant-level data from the database system 16.Such requests and updates can involve sending one or more queries totenant database 22 or system database 24. The database system 16 (forexample, an application server 100 in the database system 16) canautomatically generate one or more SQL statements (for example, one ormore SQL queries) designed to access the desired information. Systemdatabase 24 can generate query plans to access the requested data fromthe database. The term “query plan” generally refers to one or moreoperations used to access information in a database system.

Each database can generally be viewed as a collection of objects, suchas a set of logical tables, containing data fitted into predefined orcustomizable categories. A “table” is one representation of a dataobject, and may be used herein to simplify the conceptual description ofobjects and custom objects according to some implementations. It shouldbe understood that “table” and “object” may be used interchangeablyherein. Each table generally contains one or more data categorieslogically arranged as columns or fields in a viewable schema. Each rowor element of a table can contain an instance of data for each categorydefined by the fields. For example, a CRM database can include a tablethat describes a customer with fields for basic contact information suchas name, address, phone number, fax number, etc. Another table candescribe a purchase order, including fields for information such ascustomer, product, sale price, date, etc. In some MTS implementations,standard entity tables can be provided for use by all tenants. For CRMdatabase applications, such standard entities can include tables forcase, account, contact, lead, and opportunity data objects, eachcontaining pre-defined fields. As used herein, the term “entity” alsomay be used interchangeably with “object” and “table.”

In some MTS implementations, tenants are allowed to create and storecustom objects, or may be allowed to customize standard entities orobjects, for example by creating custom fields for standard objects,including custom index fields. Commonly assigned U.S. Pat. No.7,779,039, titled CUSTOM ENTITIES AND FIELDS IN A MULTI-TENANT DATABASESYSTEM, issued on Aug. 17, 2010, and hereby incorporated by referenceherein in its entirety and for all purposes, teaches systems and methodsfor creating custom objects as well as customizing standard objects in amulti-tenant database system. In some implementations, for example, allcustom entity data rows are stored in a single multi-tenant physicaltable, which may contain multiple logical tables per organization. It istransparent to customers that their multiple “tables” are in fact storedin one large table or that their data may be stored in the same table asthe data of other customers.

FIG. 2A shows a system diagram illustrating example architecturalcomponents of an on-demand database service environment 200 according tosome implementations. A client machine communicably connected with thecloud 204, generally referring to one or more networks in combination,as described herein, can communicate with the on-demand database serviceenvironment 200 via one or more edge routers 208 and 212. A clientmachine can be any of the examples of user systems 12 described above.The edge routers can communicate with one or more core switches 220 and224 through a firewall 216. The core switches can communicate with aload balancer 228, which can distribute server load over different pods,such as the pods 240 and 244. The pods 240 and 244, which can eachinclude one or more servers or other computing resources, can performdata processing and other operations used to provide on-demand services.Communication with the pods can be conducted via pod switches 232 and236. Components of the on-demand database service environment cancommunicate with database storage 256 through a database firewall 248and a database switch 252.

As shown in FIGS. 2A and 2B, accessing an on-demand database serviceenvironment can involve communications transmitted among a variety ofdifferent hardware or software components. Further, the on-demanddatabase service environment 200 is a simplified representation of anactual on-demand database service environment. For example, while onlyone or two devices of each type are shown in FIGS. 2A and 2B, someimplementations of an on-demand database service environment can includeanywhere from one to several devices of each type. Also, the on-demanddatabase service environment need not include each device shown in FIGS.2A and 2B, or can include additional devices not shown in FIGS. 2A and2B.

Additionally, it should be appreciated that one or more of the devicesin the on-demand database service environment 200 can be implemented onthe same physical device or on different hardware. Some devices can beimplemented using hardware or a combination of hardware and software.Thus, terms such as “data processing apparatus,” “machine,” “server,”“device,” and “processing device” as used herein are not limited to asingle hardware device; rather, references to these terms can includeany suitable combination of hardware and software configured to providethe described functionality.

The cloud 204 is intended to refer to a data network or multiple datanetworks, often including the Internet. Client machines communicablyconnected with the cloud 204 can communicate with other components ofthe on-demand database service environment 200 to access servicesprovided by the on-demand database service environment. For example,client machines can access the on-demand database service environment toretrieve, store, edit, or process information. In some implementations,the edge routers 208 and 212 route packets between the cloud 204 andother components of the on-demand database service environment 200. Forexample, the edge routers 208 and 212 can employ the Border GatewayProtocol (BGP). The BGP is the core routing protocol of the Internet.The edge routers 208 and 212 can maintain a table of Internet Protocol(IP) networks or ‘prefixes,’ which designate network reachability amongautonomous systems on the Internet.

In some implementations, the firewall 216 can protect the innercomponents of the on-demand database service environment 200 fromInternet traffic. The firewall 216 can block, permit, or deny access tothe inner components of the on-demand database service environment 200based upon a set of rules and other criteria. The firewall 216 can actas one or more of a packet filter, an application gateway, a statefulfilter, a proxy server, or any other type of firewall.

In some implementations, the core switches 220 and 224 are high-capacityswitches that transfer packets within the on-demand database serviceenvironment 200. The core switches 220 and 224 can be configured asnetwork bridges that quickly route data between different componentswithin the on-demand database service environment. In someimplementations, the use of two or more core switches 220 and 224 canprovide redundancy or reduced latency.

In some implementations, the pods 240 and 244 perform the core dataprocessing and service functions provided by the on-demand databaseservice environment. Each pod can include various types of hardware orsoftware computing resources. An example of the pod architecture isdiscussed in greater detail with reference to FIG. 2B. In someimplementations, communication between the pods 240 and 244 is conductedvia the pod switches 232 and 236. The pod switches 232 and 236 canfacilitate communication between the pods 240 and 244 and clientmachines communicably connected with the cloud 204, for example, viacore switches 220 and 224. Also, the pod switches 232 and 236 mayfacilitate communication between the pods 240 and 244 and the databasestorage 256. In some implementations, the load balancer 228 candistribute workload between the pods 240 and 244. Balancing theon-demand service requests between the pods can assist in improving theuse of resources, increasing throughput, reducing response times, orreducing overhead. The load balancer 228 may include multilayer switchesto analyze and forward traffic.

In some implementations, access to the database storage 256 is guardedby a database firewall 248. The database firewall 248 can act as acomputer application firewall operating at the database applicationlayer of a protocol stack. The database firewall 248 can protect thedatabase storage 256 from application attacks such as SQL injection,database rootkits, and unauthorized information disclosure. In someimplementations, the database firewall 248 includes a host using one ormore forms of reverse proxy services to proxy traffic before passing itto a gateway router. The database firewall 248 can inspect the contentsof database traffic and block certain content or database requests. Thedatabase firewall 248 can work on the SQL application level atop theTCP/IP stack, managing applications' connection to the database or SQLmanagement interfaces as well as intercepting and enforcing packetstraveling to or from a database network or application interface.

In some implementations, communication with the database storage 256 isconducted via the database switch 252. The multi-tenant database storage256 can include more than one hardware or software components forhandling database queries. Accordingly, the database switch 252 candirect database queries transmitted by other components of the on-demanddatabase service environment (for example, the pods 240 and 244) to thecorrect components within the database storage 256. In someimplementations, the database storage 256 is an on-demand databasesystem shared by many different organizations as described above withreference to FIGS. 1A and 1B.

FIG. 2B shows a system diagram further illustrating examplearchitectural components of an on-demand database service environmentaccording to some implementations. The pod 244 can be used to renderservices to a user of the on-demand database service environment 200. Insome implementations, each pod includes a variety of servers or othersystems. The pod 244 includes one or more content batch servers 264,content search servers 268, query servers 282, file servers 286, accesscontrol system (ACS) servers 280, batch servers 284, and app servers288. The pod 244 also can include database instances 290, quick filesystems (QFS) 292, and indexers 294. In some implementations, some orall communication between the servers in the pod 244 can be transmittedvia the pod switch 236.

In some implementations, the app servers 288 include a hardware orsoftware framework dedicated to the execution of procedures (forexample, programs, routines, scripts) for supporting the construction ofapplications provided by the on-demand database service environment 200via the pod 244. In some implementations, the hardware or softwareframework of an app server 288 is configured to execute operations ofthe services described herein, including performance of the blocks ofvarious methods or processes described herein. In some alternativeimplementations, two or more app servers 288 can be included andcooperate to perform such methods, or one or more other serversdescribed herein can be configured to perform the disclosed methods.

The content batch servers 264 can handle requests internal to the pod.Some such requests can be long-running or not tied to a particularcustomer. For example, the content batch servers 264 can handle requestsrelated to log mining, cleanup work, and maintenance tasks. The contentsearch servers 268 can provide query and indexer functions. For example,the functions provided by the content search servers 268 can allow usersto search through content stored in the on-demand database serviceenvironment. The file servers 286 can manage requests for informationstored in the file storage 298. The file storage 298 can storeinformation such as documents, images, and binary large objects (BLOBs).By managing requests for information using the file servers 286, theimage footprint on the database can be reduced. The query servers 282can be used to retrieve information from one or more file systems. Forexample, the query servers 282 can receive requests for information fromthe app servers 288 and transmit information queries to the network filesystems (NFS) 296 located outside the pod.

The pod 244 can share a database instance 290 configured as amulti-tenant environment in which different organizations share accessto the same database. Additionally, services rendered by the pod 244 maycall upon various hardware or software resources. In someimplementations, the ACS servers 280 control access to data, hardwareresources, or software resources. In some implementations, the batchservers 284 process batch jobs, which are used to run tasks at specifiedtimes. For example, the batch servers 284 can transmit instructions toother servers, such as the app servers 288, to trigger the batch jobs.

In some implementations, the QFS 292 is an open source file systemavailable from Sun Microsystems, Inc. The QFS can serve as arapid-access file system for storing and accessing information availablewithin the pod 244. The QFS 292 can support some volume managementcapabilities, allowing many disks to be grouped together into a filesystem. File system metadata can be kept on a separate set of disks,which can be useful for streaming applications where long disk seekscannot be tolerated. Thus, the QFS system can communicate with one ormore content search servers 268 or indexers 294 to identify, retrieve,move, or update data stored in the NFS 296 or other storage systems.

In some implementations, one or more query servers 282 communicate withthe NFS 296 to retrieve or update information stored outside of the pod244. The NFS 296 can allow servers located in the pod 244 to accessinformation to access files over a network in a manner similar to howlocal storage is accessed. In some implementations, queries from thequery servers 282 are transmitted to the NFS 296 via the load balancer228, which can distribute resource requests over various resourcesavailable in the on-demand database service environment. The NFS 296also can communicate with the QFS 292 to update the information storedon the NFS 296 or to provide information to the QFS 292 for use byservers located within the pod 244.

In some implementations, the pod includes one or more database instances290. The database instance 290 can transmit information to the QFS 292.When information is transmitted to the QFS, it can be available for useby servers within the pod 244 without using an additional database call.In some implementations, database information is transmitted to theindexer 294. Indexer 294 can provide an index of information availablein the database instance 290 or QFS 292. The index information can beprovided to the file servers 286 or the QFS 292.

FIG. 3 illustrates a diagrammatic representation of a machine in theexemplary form of a computer system 300 within which a set ofinstructions (e.g., for causing the machine to perform any one or moreof the methodologies discussed herein) may be executed. In alternativeimplementations, the machine may be connected (e.g., networked) to othermachines in a LAN, a WAN, an intranet, an extranet, or the Internet. Themachine may operate in the capacity of a server or a client machine inclient-server network environment, or as a peer machine in apeer-to-peer (or distributed) network environment. The machine may be apersonal computer (PC), a tablet PC, a set-top box (STB), a PDA, acellular telephone, a web appliance, a server, a network router, switchor bridge, or any machine capable of executing a set of instructions(sequential or otherwise) that specify actions to be taken by thatmachine. Further, while only a single machine is illustrated, the term“machine” shall also be taken to include any collection of machines thatindividually or jointly execute a set (or multiple sets) of instructionsto perform any one or more of the methodologies discussed herein. Someor all of the components of the computer system 300 may be utilized byor illustrative of any of the electronic components described herein(e.g., any of the components illustrated in or described with respect toFIGS. 1A, 1B, 2A, and 2B).

The exemplary computer system 300 includes a processing device(processor) 302, a main memory 304 (e.g., ROM, flash memory, dynamicrandom access memory (DRAM) such as synchronous DRAM (SDRAM) or RambusDRAM (RDRAM), etc.), a static memory 306 (e.g., flash memory, staticrandom access memory (SRAM), etc.), and a data storage device 320, whichcommunicate with each other via a bus 310.

Processor 302 represents one or more general-purpose processing devicessuch as a microprocessor, central processing unit, or the like. Moreparticularly, the processor 302 may be a complex instruction setcomputing (CISC) microprocessor, reduced instruction set computing(RISC) microprocessor, very long instruction word (VLIW) microprocessor,or a processor implementing other instruction sets or processorsimplementing a combination of instruction sets. The processor 302 mayalso be one or more special-purpose processing devices such as anapplication specific integrated circuit (ASIC), a field programmablegate array (FPGA), a digital signal processor (DSP), network processor,or the like. The processor 302 is configured to execute instructions 340for performing the operations and steps discussed herein.

The computer system 300 may further include a network interface device308. The computer system 300 also may include a video display unit 312(e.g., a liquid crystal display (LCD), a cathode ray tube (CRT), or atouch screen), an alphanumeric input device 314 (e.g., a keyboard), acursor control device 316 (e.g., a mouse), and a signal generationdevice 322 (e.g., a speaker).

Power device 318 may monitor a power level of a battery used to powerthe computer system 300 or one or more of its components. The powerdevice 318 may provide one or more interfaces to provide an indicationof a power level, a time window remaining prior to shutdown of computersystem 300 or one or more of its components, a power consumption rate,an indicator of whether computer system is utilizing an external powersource or battery power, and other power related information. In someimplementations, indications related to the power device 318 may beaccessible remotely (e.g., accessible to a remote back-up managementmodule via a network connection). In some implementations, a batteryutilized by the power device 318 may be an uninterruptable power supply(UPS) local to or remote from computer system 300. In suchimplementations, the power device 318 may provide information about apower level of the UPS.

The data storage device 320 may include a computer-readable storagemedium 324 (e.g., a non-transitory computer-readable storage medium) onwhich is stored one or more sets of instructions 340 (e.g., software)embodying any one or more of the methodologies or functions describedherein. These instructions 340 may also reside, completely or at leastpartially, within the main memory 304 and/or within the processor 302during execution thereof by the computer system 300, the main memory304, and the processor 302 also constituting computer-readable storagemedia. These instructions 340 may further be transmitted or receivedover a network 330 (e.g., the network 14) via the network interfacedevice 308. While the computer-readable storage medium 324 is shown inan exemplary implementation to be a single medium, it is to beunderstood that the computer-readable storage medium 324 may include asingle medium or multiple media (e.g., a centralized or distributeddatabase, and/or associated caches and servers) that store the one ormore sets of instructions 340.

PIPELINE ARCHITECTURE FOR EXECUTING COMPUTATION SEQUENCES

Referring now to FIG. 4, an exemplary pipeline architecture 400 forprocessing user-defined pipelines according to some implementations isillustrated. The data framework 400 illustrates a network adaptor 420, apipeline engine 430, and one or more user-defined pipelines 440A-440C.In some implementations, the network adapter 420 is configured to ingestand transmit data to other devices across a communications network. Forexample, the network can read data from the communications network(which may include user requests to execute one or more user-definedpipelines 440A-440C) and deserialize the data into a pipeline dispatchrequest. A pipeline dispatch request is a message that identifies apipeline (e.g., a user-defined pipeline to be executed) and has a formatthat facilitates sending stage-specific data between stage invocationsby the pipeline engine 430. In some implementations, the network adapter420 utilizes an internal pipeline channel 425 to transmit pipelinedispatch requests to other framework application instances. In someimplementations, such framework application instances may handle theevaluation of deep learning models, communicate with external databases,or enable additional scaling to handle the evaluation and execution ofuser-defined pipelines 440A-440C.

In some implementations, the network adapter 420 then transmits thepipeline dispatch request to the pipeline engine 430. In someimplementations, the pipeline engine 430 manages the execution ofuser-defined pipelines 440A-440C. The pipeline engine 430 may be capableof running multiple user-defined pipelines 440A-440C, as well asdetermining which pipeline to execute and how many steps/stages of thepipeline to execute. In some implementations, each node running thepipeline architecture 400 can implement a pipeline engine 430 to executethe user-defined pipelines 440A-440C. Once the pipeline engine 430receives the pipeline dispatch request, the pipeline engine 430 can thenfetch the corresponding user-defined pipeline and advance the dispatchrequest through the corresponding stage in the user-defined pipeline. Insome implementations, the pipeline engine 430 decides whether tocontinue evaluating the dispatch request returned from the pipelineevaluation or send the dispatch request back to the network adapter 420.The logic by which the pipeline 430 makes these decisions is describedin greater detail below with respect to FIG. 5.

In some implementations, the network adaptor 420 adapts the pipelineengine 430 to an underlying network communication pattern. This allows,for example, underlying communication technologies/networking frameworksto be swapped out with no impact on the user-defined pipelines440A-440C. An advantage of the present implementations is the ability toabstract pipeline execution into the pipeline engine 430. For example,in some implementations, the pipeline engine provides a layer ofabstraction between the user-defined pipelines 440A-440C and thenetwork. In such implementations, one or more user-defined pipelines440A-440C may be executed without any knowledge of which underlyingnetwork framework is used. This allows a user or a team of users runningan application to determine what underlying networking framework theywish to use. For example, running the framework as a Kafka applicationversus a Storm application differs in terms of how the network adaptor420 is implemented. A further advantage is that in some implementations,the pipeline engine 430 can forego network communication in favor ofcomputing successive one or more successive stages of a user-definedpipeline locally. This allows for execution of user-defined pipelinesthat dramatically reduces network overhead. The logic for executing eachuser-defined pipeline 440A-440C may be the same and is implemented bythe pipeline engine 430.

In some implementations, each of the user-defined pipelines 440A-440Care built from one or more stages. It is noted that the threeuser-defined pipelines are illustrative, and that at any given time anarbitrary number of user-defined pipelines may be accessible to thepipeline architecture 400. Each stage of a given pipeline is auser-defined function that maps an input type to an output type. Theuser can specify serialization routines that are implemented in betweenstages, and may be implemented at the beginning and end of the pipeline.In some implementations, the serialization routines allow the user toutilize any binary format for their data, including a customized formatdefined by the user.

In some implementations, information pertaining to the underlyingnetwork framework is not provided to the user or selectable as an optionby the user at the time that the user generates their pipeline andrequest to execute the pipeline. For example, during and after executionof a user-defined pipeline, the system avoids providing data to the userdescriptive of the underlying framework used. The network adapter 420may benefit from such abstraction as it can remain unaware of theuser-defined pipelines or the logic of the pipeline engine 430. Thenetwork adapter 420 behavior can be reduced to communication over thecommunications network and calling the pipeline engine 430 with datathat has the network adaptor 420 has deserialized from thecommunications network. In such implementations, a network adaptor 420written/generated for a particular framework, such as Apache Hadoop,could be usable for any set of user-defined pipelines, rendering thepipeline architecture 400 usable across multiple application settings.

Reference is now made to FIG. 5, which is a flow diagram illustrating anexemplary method 500 for processing dispatch requests from a networkadapter according to some implementations. The method 500 may beperformed by processing logic comprising hardware (e.g., circuitry,dedicated logic, programmable logic, microcode, etc.), software (such asinstructions run on a processing device), or a combination thereof. Insome implementations, the method 500 may be performed by a databasesystem (e.g., the database system 16), and or a distributed dataprocessing system implementing, for example, the pipeline architecture400 via one or more processing devices. It is to be understood thatthese implementations are merely exemplary, and that other devices mayperform some or all of the functionality described.

At block 510, the pipeline engine (e.g., the pipeline engine 430)receives a dispatch request from a network adapter (e.g., the networkadaptor 420), which is descriptive of or identifies one or moreuser-defined pipelines (e.g., one or more of the user-defined pipelines440A-440C).

At block 520, the pipeline engine evaluates the dispatch request. Forexample, in some implementations, the pipeline engine 430 may evaluatethe dispatch request by advancing the associated user-defined pipelinethrough at least one stage.

At block 530, the pipeline engine determines whether the user-definedpipeline associated with the dispatch request should be executed locallyby the pipeline engine, or if the dispatch request should be returned tothe network adapter. In some implementations, the pipeline engine tracksthe execution times of dispatch requests by itself or by other pipelineengines across the communications network. In some implementations, thepipeline engine computes an expected computation time based on measurednetwork latencies. In some implementations, the pipeline engine mayreceive or retrieve metadata from messages exchanged betweencomputational nodes. The pipeline engine may extract information fromthe metadata to estimate computational times for local computation,which may be used to determine whether local execution is more efficientthan further serialization and deserialization. In some implementations,multiple servers may be implemented by the pipeline engine to executeone or more stages of the user-defined pipeline. Depending on thelatency of each server, the pipeline engine may opt to have one serverperform local execution more frequently than the other server, or mayopt to transmit the dispatch request back to the network adapter toavoid overloading the servers.

In some implementations, by advancing the associated user-definedpipeline through at least one stage, the pipeline engine may estimate atotal amount of time required to fully execute the user-definedpipeline.

The pipeline engine may utilize the tracked or estimated execution timesto determine, on a per-pipeline basis, whether it is more efficient tolocally execute of one or more stages of the user-defined pipeline or totransmit the dispatch request back to the network adapter to identify adifferent pipeline engine or device to execute the user-definedpipeline. For example, if average execution time for the stages of agiven pipeline is around 1 millisecond and serialization time is 2milliseconds, the pipeline engine can optimize execution time of thegiven pipeline by executing or continuing to execute multiple stages ofthe given pipeline locally. By locally executing the pipeline instead oftransmitting the dispatch request to the network adapter, the pipelineengine can advantageously reduce the impact of serialization anddeserialization on the overall end-to-end pipeline execution time.Further to the preceding example, a pipeline with three stages couldsave, for example, 8 milliseconds by continued local execution comparedto transmitting the dispatch request back to the network adapter. Thissavings in execution time could translate into three additional completepipeline executions for every two dispatch requests that are processedthrough their respective pipelines (i.e., a 150% speed increase).

In some situations, local execution by the pipeline engine may be lessefficient than the overhead generated through serialization anddeserialization. For example, this could be true in the situation wherea given user-defined pipeline did not interact with external servicessuch as deep learning models or databases. Interactions with externalservices are often more efficiently executed on a separate device thanlocally on the same device. For example, if a dispatch request is to berun through a deep learning model requiring specialized hardware, butonly 5% of all dispatches require this hardware, it may be moretime-efficient to send such dispatch requests to a device withspecialized hardware rather than requiring all devices to have thespecialized hardware (e.g., which can be an order of magnitude moreexpensive to run).

In some implementations, a user may at least partially override thedecision logic at block 530, for example, by specifying that one or morestages of the user-defined pipeline are to be executed locally by thepipeline engine. In some implementations, the user may request atruntime that one or more stages of the pipeline engine be executedlocally. In some implementations, the user may specify specific stagesof the pipeline that are to be executed locally.

At block 530, if the pipeline engine determines that the user-definedpipeline is to be executed locally, then the method 500 proceeds toblock 540 where the pipeline engine evaluates or continues to evaluatethe dispatch request. Otherwise, the method proceeds to block 550, wherethe dispatch request is transmitted back to the network adapter, whichmay then select a separate pipeline engine or device to which thedispatch request is transmitted.

Reference is now made to FIG. 6, which is a flow diagram illustrating anexemplary method 600 for performing sequences of computations in aninfrastructure-agnostic manner according to some implementations. Themethod 600 may be performed by processing logic comprising hardware(e.g., circuitry, dedicated logic, programmable logic, microcode, etc.),software (such as instructions run on a processing device), or acombination thereof. In some implementations, the method 600 may beperformed by a database system (e.g., the database system 16), and or adistributed data processing system implementing, for example, thepipeline architecture 400 via one or more processing devices. It is tobe understood that these implementations are merely exemplary, and thatother devices may perform some or all of the functionality described.

Referring to FIG. 6, at block 610, a pipeline engine (e.g., the pipelineengine 430) receives, from a network adapter (e.g., the network adapter420), a dispatch request for executing a user-defined pipeline. In someimplementations, the dispatch request is agnostic to an underlyingnetwork infrastructure by which the user-defined pipeline is to beexecuted.

In some implementations, the dispatch request is descriptive of a userrequest comprising the user-defined pipeline and one or moreuser-specified serialization routines to be performed at various stagesin the user-defined pipeline.

At block 620, a performance metric is computed based on the dispatchrequest (e.g., by the pipeline engine). In some implementations, theperformance metric is a network latency. The user-specific pipeline maybe executed locally by the pipeline engine in response to determiningthat local execution is computationally faster or more efficient thanthe user-specific pipeline being transmitted by the network adapter to adifferent pipeline engine for execution. In some implementations,determining that local execution is computationally faster or moreefficient than the user-specific pipeline being transmitted by thenetwork adapter to a different pipeline engine for execution comprisesestimating a local execution time based on an execution time of at leastone stage of the user-specific pipeline by the pipeline engine.

At block 630, a determination is made, based at least partially on theperformance metric, whether to execute the user-defined pipeline locallyby the pipeline engine or transmit the dispatch request back to thenetwork adapter.

In some implementations, one or more stages of the user-defined pipelineare executed locally by the pipeline engine based on a user-specifiedrequirement. In some implementations, executing the user-definedpipeline locally by the pipeline engine includes requesting one or moreexternal resources available to the pipeline engine to improve oroptimize execution of the user-defined pipeline. In someimplementations, the dispatch request is agnostic to the externalresources requested.

For simplicity of explanation, the methods of this disclosure aredepicted and described as a series of acts. However, acts in accordancewith this disclosure can occur in various orders and/or concurrently,and with other acts not presented and described herein. Furthermore, notall illustrated acts may be required to implement the methods inaccordance with the disclosed subject matter. In addition, those skilledin the art will understand and appreciate that the methods couldalternatively be represented as a series of interrelated states via astate diagram or events. Additionally, it should be appreciated that themethods disclosed in this specification are capable of being stored onan article of manufacture to facilitate transporting and transferringinstructions for performing such methods to computing devices. The term“article of manufacture,” as used herein, is intended to encompass acomputer program accessible from any computer-readable device or storagemedia.

In the foregoing description, numerous details are set forth. It will beapparent, however, to one of ordinary skill in the art having thebenefit of this disclosure, that the present disclosure may be practicedwithout these specific details. While specific implementations have beendescribed herein, it should be understood that they have been presentedby way of example only, and not limitation. The breadth and scope of thepresent application should not be limited by any of the implementationsdescribed herein, but should be defined only in accordance with thefollowing and later-submitted claims and their equivalents. Indeed,other various implementations of and modifications to the presentdisclosure, in addition to those described herein, will be apparent tothose of ordinary skill in the art from the foregoing description andaccompanying drawings. Thus, such other implementations andmodifications are intended to fall within the scope of the presentdisclosure.

Furthermore, although the present disclosure has been described hereinin the context of a particular implementation in a particularenvironment for a particular purpose, those of ordinary skill in the artwill recognize that its usefulness is not limited thereto and that thepresent disclosure may be beneficially implemented in any number ofenvironments for any number of purposes. Accordingly, the claims setforth below should be construed in view of the full breadth and spiritof the present disclosure as described herein, along with the full scopeof equivalents to which such claims are entitled.

1. A computer-implemented method comprising: receiving, from a networkadapter by a pipeline engine of a pipeline architecture, a dispatchrequest for executing a user-defined pipeline, wherein the dispatchrequest is agnostic to an underlying network infrastructure by which theuser-defined pipeline is to be executed; computing a performance metricbased on the dispatch request and by information extracted from metadataassociated with messages exchanged between computational nodes of thepipeline architecture; and determining, based at least partially on theperformance metric, whether to execute the user-defined pipeline locallyby the pipeline engine or transmit the dispatch request back to thenetwork adapter.
 2. The computer-implemented method of claim 1, whereinthe dispatch request is descriptive of a user request comprising theuser-defined pipeline and one or more user-specified serializationroutines to be performed at various stages in the user-defined pipeline.3. The computer-implemented method of claim 1, further comprising:executing one or more stages of the user-defined pipeline locally by thepipeline engine based on a user-specified requirement.
 4. Thecomputer-implemented method of claim 1, wherein the performance metricis a network latency, and wherein the user-defined pipeline is executedlocally by the pipeline engine in response to determining that localexecution is computationally faster or more efficient than theuser-defined pipeline being transmitted by the network adapter to adifferent pipeline engine for execution.
 5. The computer-implementedmethod of claim 4, wherein determining that local execution iscomputationally faster or more efficient than the user-defined pipelinebeing transmitted by the network adapter to a different pipeline enginefor execution comprises estimating a local execution time based on anexecution time of at least one stage of the user-defined pipeline by thepipeline engine.
 6. The computer-implemented method of claim 1, whereinexecuting the user-defined pipeline locally by the pipeline enginecomprises requesting one or more external resources available to thepipeline engine to improve or optimize execution of the user-definedpipeline.
 7. The computer-implemented method of claim 6, wherein thedispatch request is agnostic to the one or more external resourcesrequested.
 8. A distributed data processing system comprising: one ormore processors configured to implement a pipeline engine of a pipelinearchitecture and a network adapter; and a memory device coupled to theone or more processors, the memory device having instructions storedthereon that, in response to execution by the one or more processors,cause the one or more processors to: receive, by the pipeline enginefrom the network adapter, a dispatch request for executing auser-defined pipeline, wherein the dispatch request is agnostic to anunderlying network infrastructure by which the user-defined pipeline isexecuted; compute a performance metric based on the dispatch request andby information extracted from metadata associated with messagesexchanged between computational nodes of the pipeline architecture; anddetermine, based at least partially on the performance metric, whetherto execute the user-defined pipeline locally by the pipeline engine ortransmit the dispatch request back to the network adapter.
 9. Thedistributed data processing system of claim 8, wherein the dispatchrequest is descriptive of a user request comprising the user-definedpipeline and one or more user-specified serialization routines to beperformed at various stages in the user-defined pipeline.
 10. Thedistributed data processing system of claim 8, wherein the one or moreprocessors are to further: execute one or more stages of theuser-defined pipeline locally by the pipeline engine based on auser-specified requirement.
 11. The distributed data processing systemof claim 8, wherein the performance metric is a network latency, andwherein the user-defined pipeline is to be executed locally by thepipeline engine in response to determining that local execution iscomputationally faster or more efficient than the user-defined pipelinebeing transmitted by the network adapter to a different pipeline enginefor execution.
 12. The distributed data processing system of claim 11,wherein to determine that local execution is computationally faster ormore efficient than the user-defined pipeline being transmitted by thenetwork adapter to a different pipeline engine for execution, the one ormore processors are to further estimate a local execution time based onan execution time of at least one stage of the user-defined pipeline bythe pipeline engine.
 13. The distributed data processing system of claim8, wherein to execute the user-defined pipeline locally by the pipelineengine, the one or more processors are to further request one or moreexternal resources available to the pipeline engine to improve oroptimize execution of the user-defined pipeline.
 14. The distributeddata processing system of claim 13, wherein the dispatch request isagnostic to the one or more external resources requested.
 15. Anon-transitory computer-readable storage medium having instructionsencoded thereon which, when executed by one or more processing devicesof a distributed data processing system, cause the one or moreprocessing devices to: receive, from a network adapter by a pipelineengine of a pipeline architecture, a dispatch request for executing auser-defined pipeline, wherein the dispatch request is agnostic to anunderlying network infrastructure by which the user-defined pipeline isexecuted; compute a performance metric based on the dispatch request andby information extracted from metadata associated with messagesexchanged between computational nodes of the pipeline architecture; anddetermine, based at least partially on the performance metric, whetherto execute the user-defined pipeline locally by the pipeline engine ortransmit the dispatch request back to the network adapter.
 16. Thenon-transitory computer-readable storage medium of claim 15, wherein thedispatch request is descriptive of a user request comprising theuser-defined pipeline and one or more user-specified serializationroutines to be performed at various stages in the user-defined pipeline.17. The non-transitory computer-readable storage medium of claim 15,wherein the one or more processing devices are to further: execute oneor more stages of the user-defined pipeline locally by the pipelineengine based on a user-specified requirement.
 18. The non-transitorycomputer-readable storage medium of claim 15, wherein the performancemetric is a network latency, and wherein the user-defined pipeline is tobe executed locally by the pipeline engine in response to determiningthat local execution is computationally faster or more efficient thanthe user-defined pipeline being transmitted by the network adapter to adifferent pipeline engine for execution.
 19. The non-transitorycomputer-readable storage medium of claim 18, wherein to determine thatlocal execution is computationally faster or more efficient than theuser-defined pipeline being transmitted by the network adapter to adifferent pipeline engine for execution, the one or more processingdevices are to further estimate a local execution time based on anexecution time of at least one stage of the user-defined pipeline by thepipeline engine.
 20. The non-transitory computer-readable storage mediumof claim 15, wherein to execute the user-defined pipeline locally by thepipeline engine, the one or more processing devices are to furtherrequest one or more external resources available to the pipeline engineto improve or optimize execution of the user-defined pipeline, andwherein the dispatch request is agnostic to the one or more externalresources requested.